Engelhardt Lab

We developed a general approach to identifying covariates with variance effects on a quantitative trait using a Bayesian heteroskedastic linear regression model.

The work is described in:

Dumitrascu B, Darnell G, Ayroles J, and Engelhardt BE. “A Bayesian test to identify variance effects” (submitted) [arXiv]

The BTH software, written and maintained by Bianca Dumitrascu, is publicly available: [Software]

We developed a nonparametric model-based method, Dirichlet process Gaussian process mixture model (DPGP) to jointly model data clusters with a Dirichlet process and temporal dependencies with Gaussian processes.

The work is described in:

McDowell IC, Manandhar D, Vockley CM, Schmid AK, Reddy TE, and Engelhardt BE (2018). “Clustering gene expression time series data using an infinite Gaussian process mixture model” (PLOS Computational Biology) [PDF]

The BTH software, written and maintained by Ian McDowell, is publicly available: [Software]

We developed a general framework, the coupled compound Poisson factorization (CCPF), to capture the missing-data mechanism in extremely sparse data sets by coupling a hierarchical Poisson factorization with an arbitrary data-generating model. In the context of matrix factorization, the hierarchical compound Poisson factorization (HCPF) decouples the sparsity model from the response model, allowing us to choose the most suitable distribution for the response. HCPF can capture binary, non-negative discrete, non-negative continuous, and zero-inflated continuous responses.

The work is described in:

Basbug M and Engelhardt BE (2016). “Hierarchical compound Poisson factorization” Proceedings of the International Conference on Machine Learning (ICML) [PDF]

Basbug M and Engelhardt BE. “Coupled compound Poisson factorization” (submitted) [arXiv]

The HCPF and CCPF software, written and maintained by Mehmet Basbug, is publicly available: [Software]